Artificial Intelligence Data Engineer II

Job Summary

The Artificial Intelligence Data Engineer II designs, develops, and manages scalable data pipelines and feature stores that enable AI/Machine Learning (ML) model training and deployment across the enterprise.  This position collaborates with technical team members to automate data flows, integrate structured and unstructured data sources, and optimize performance for large-scale processing.

The AI Data Engineer II also implements data quality validation, metadata management, and lineage tracking to ensure trusted data delivery for AI applications in compliance with healthcare regulations. 

Duties

Design and implement scalable data pipelines for AI/ML workloads.

Develop and deploy AI/ML solutions using Python, Snowpark, or cloud-native ML services.

Build and manage feature stores to support model training and inference.

Integrate structured and unstructured data sources from internal and external systems.

Collaborate with data scientists to understand data requirements and optimize pipelines.

Implement data quality checks, metadata tagging, and lineage tracking.

Ensure compliance with Health Insurance Portability and Accountability Act (HIPAA), Centers for Medicare and Medicaid Services (CMS), and enterprise data governance standards.

Automate data ingestion and transformation using tools like AWS Glue, Snowflake, and Informatica Data Management Cloud (IDMC).

Implement DevOps/MLOps and Continuous Integration (CI)/Continuous Delivery (CD) pipelines using git actions or similar tools.

Monitor pipeline performance and troubleshoot issues in production environments.

Contribute to backlog grooming and sprint planning for AI data initiatives.

Perform other duties as assigned.

Duties Continued

Education Required

Education Preferred

Experience

Required: 

At least 5 years of experience in data engineering.

 At least 2 years of experience focused on AI/ML data pipelines.

Hands on experience working on GenAI projects (chatbot implementations, Natural Language Processing (NLP), Sentiment Analysis, recommendation systems, anomaly detection etc.

Preferred: 

Experience in health plan payer systems and regulatory data handling.

Experience with Fast Healthcare Interoperability Resources (FHIR), Health Level Seven (HL7), HIPAA compliance, and healthcare data standards.

Skills

Required:

Proficient skills in Python, SQL, Spark, AWS (Glue, S3, Lambda), Snowflake (Snowpark Container Services), IDMC, prompt engineering, model inference and fine-tuning, RAG and working with MCP, Vector databases.

Proficient technical and data engineering skills

Solid understanding of supervised and unsupervised machine learning methods, feature engineering, model evaluation, and validation techniques.

Ability to operationalize models in production environments, including basic MLOps practices (version control, CI/CD, reproducibility).

Ability to communicate complex AI/ML concepts effectively to non-technical stakeholders.

Excellent documentation skills, ensuring reproducibility, clarity of assumptions, and transparency of model design.

Strong collaboration skills, with proven ability to work cross-functionally with key stakeholders.

Analytical problem-solving skills with the ability to translate business challenges into actionable AI/ML solutions.

Effective written and verbal communication skills, including documentation of modeling processes, assumptions, and results.

Preferred:

Experience with FHIR, HL7, HIPAA compliance, and healthcare data standards.

Licenses/Certifications Required

Licenses/Certifications Preferred

Required Training

Required:

Data pipeline development and cloud platform training.

Preferred:

Healthcare compliance and regulatory training.

Physical Requirements

Additional Information